Bifocal Modeling: Promoting Authentic Scientific Inquiry Through Exploring and Comparing Real and Ideal Systems Linked in Real-Time

Part of the Gaming Media and Social Effects book series (GMSE)


The improvement of STEM education through new pedagogies and technologies has been the chief concern of policy-makers and educators for the past decades. Common threads among the proposed solutions have been to promote inquiry, discovery, and authentic scientific practices in the classroom. In this chapter, we present a novel inquiry-based framework which combines computer simulations and real-world sensing in real-time: bifocal modeling. Even though educational researchers have come to realize the potential of simulations, computer models, and probeware separately, little research and design have been done on the combination of these new technologies. When creating a bifocal model, students build a computer simulation and the analogous sensing apparatus, and link them in real-time, being able to validate, compare, and refine their conceptual models using data. In this chapter, I will focus on the technical and pedagogical aspects of this framework, describe several example models, and discuss four pilot studies, which suggest that the synergy between physical and simulated systems catalyzes further inquiry toward a deeper understanding of the scientific phenomena.


Computer modeling Sensing Constructivism Physical computing Bifocal modeling Constructionism Probeware Scientific inquiry 



Thanks to Shima Salehi, Tamar Furhmann, Bertrand Schneider, and Daniel Greene for their work in the research and earlier versions of this chapter, and Elayne Weissler-Martello for her proofreading work. Special thanks to the students who created the bifocal models shown in this article. This work is funded by the NSF CAREER Award #1055130, NSF DRL 1020101, the Stanford MediaX program, and the Stanford Lemann Center for Educational Entrepreneurship and Innovation in Brazil.


  1. Baird D (2006) Thing knowledge: a philosophy of scientific instruments. University of California Press, BerkeleyGoogle Scholar
  2. Birchfield D, Megowan-Romanowicz C (2009) Earth science learning in SMALLab: a design experiment for mixed reality. Int J Comput-Support Collab Learn 4(4):403–421CrossRefGoogle Scholar
  3. Blake C, Scanlon E (2007) Reconsidering simulations in science education at a distance: features of effective use. J Comput Assist Learn 23(6):491–502CrossRefGoogle Scholar
  4. Blikstein P (2009) An atom is known by the company it keeps: content, representation and pedagogy within the epistemic revolution of the complexity sciences. (PhD. dissertation), Northwestern University, Evanston, ILGoogle Scholar
  5. Blikstein P (2010) Connecting the science classroom and tangible interfaces: the bifocal modeling framework. In: Proceedings of the 9th International Conference of the Learning Sciences, Chicago, IL, pp 128–130Google Scholar
  6. Blikstein P (2012) Bifocal modeling: a study on the learning outcomes of comparing physical and computational models linked in real time. In: Proceedings of the 14th ACM International Conference on Multimodal Interaction, Los Angeles, CA, pp 257–264Google Scholar
  7. Blikstein P, Wilensky U (2006) ‘Hybrid modeling’: advanced scientific investigations linking computer models and real-world sensing (an interactive poster). In: Proceedings of the 7th International Conference on Learning Sciences, Bloomington, IN, pp 890–891Google Scholar
  8. Blikstein P, Wilensky U (2007) Bifocal modeling: a framework for combining computer modeling, robotics and real-world sensing. Paper presented at the annual meeting of the American Research Education Association, Chicago, ILGoogle Scholar
  9. Blikstein P, Wilensky U (2009) An atom is known by the company it keeps: a constructionist learning environment for materials science using agent-based modeling. Int J Comput Math Learn 14(2):81–119CrossRefGoogle Scholar
  10. Blikstein P, Wilensky U (2010) MaterialSim: a constructionist agent-based modeling approach to engineering education. In: Jacobson MJ, Reimann P (eds) Designs for learning environments of the future: international perspectives from the learning sciences. Springer, New York, pp 17–60CrossRefGoogle Scholar
  11. Blikstein P, Fuhrmann T, Greene D, Salehi S (2012) Bifocal modeling: mixing real and virtual labs for advanced science learning. In: Proceedings of the 11th International Conference on Interaction Design and Children, Bremen, Germany, pp 296–299Google Scholar
  12. Bryson J, Ando Y, Lehmann H (2007) Agent-based modelling as scientific method: a case study analyzing primate social behaviour. Philos Trans R Soc B: Biol Sci 362(1485):1685CrossRefGoogle Scholar
  13. Cagnacci F, Boitani L, Powell RA, Boyce MS (2010) Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Philos Trans R Soc B: Biol Sci 365(1550):2157–2162CrossRefGoogle Scholar
  14. Chen H-T, Tien M-C, Chen Y-W, Tsai W-J, Lee S-Y (2009) Physics-based ball tracking and 3D trajectory reconstruction with applications to shooting location estimation in basketball video. J Vis Commun Image Represent 20(3):204–216CrossRefGoogle Scholar
  15. Collins A, Ferguson W (1993) Epistemic forms and epistemic games: structures and strategies to guide inquiry. Educ Psychol 28(1):25–42CrossRefGoogle Scholar
  16. Confrey J (2005) The evolution of design studies as methodology. In: Sawyer K (ed) The Cambridge handbook of the learning sciences. Cambridge University Press, Cambridge, pp 135–151Google Scholar
  17. de Jong T, Linn MC, Zacharia ZC (2013) Physical and virtual laboratories in science and engineering education. Science 340(6130):305–308CrossRefGoogle Scholar
  18. DeBoer GE (2000) Scientific literacy: another look at its historical and contemporary meanings and its relationship to science education reform. J Res Sci Teach 37(6):582–601CrossRefGoogle Scholar
  19. Duschl RA, Grandy RE (2008) Teaching scientific inquiry: recommendations for research and implementation. Sense Publishers, The NetherlandsGoogle Scholar
  20. Edelson DC (2002) Design research: what we learn when we engage in design. J Learn Sci 11(1):105–121CrossRefMathSciNetGoogle Scholar
  21. Finkelstein N, Adams W, Keller C, Kohl P, Perkins K, Podolefsky N, Reid S, LeMaster R (2005) When learning about the real world is better done virtually: a study of substituting computer simulations for laboratory equipment. Phys Rev Spec Top-Phys Educ Res 1(1):010103CrossRefGoogle Scholar
  22. Gire E, Carmichael A, Chini JJ, Rouinfar A, Rebello S, Smith G, Puntambekar S (2010) The effects of physical and virtual manipulatives on students’ conceptual learning about pulleys. In: Proceedings of the 9th International Conference of the Learning Sciences, Chicago, IL, (pp. 937–943)Google Scholar
  23. Grosslight L, Unger C, Jay E, Smith C (1991) Understanding models and their use in science: conceptions of middle and high school students and experts. J Res Sci Teach 28(9):799–822CrossRefGoogle Scholar
  24. Hmelo-Silver CE, Marathe S, Liu L (2007) Fish swim, rocks sit, and lungs breathe: expert-novice understanding of complex systems. J Learn Sci 16(3):307–331CrossRefGoogle Scholar
  25. Hodson D (1996) Laboratory work as scientific method: three decades of confusion and distortion. J Curriculum Stud 28(2):115–135CrossRefGoogle Scholar
  26. Hodson D (1998) Science fiction: the continuing misrepresentation of science in the school curriculum. Curriculum Stud 6(2):191–216Google Scholar
  27. Hofer BK, Pintrich PR (1997) The development of epistemological theories: beliefs about knowledge and knowing and their relation to learning. Rev Educ Res 67(1):88–140CrossRefGoogle Scholar
  28. Hutchins E (1995) How a cockpit remembers its speed. Cogn Sci 19(3):265–288CrossRefGoogle Scholar
  29. Ingham AM, Gilbert JK (1991) The use of analogue models by students of chemistry at higher education level. Int J Sci Educ 13(2):193–202CrossRefGoogle Scholar
  30. Jaakkola T, Nurmi S (2008) Fostering elementary school students’ understanding of simple electricity by combining simulation and laboratory activities. J Comput Assist Learn 24(4):271–283CrossRefGoogle Scholar
  31. Johnson KS, Needoba JA, Riser SC, Showers WJ (2007) Chemical sensor networks for the aquatic environment. Chem Rev 107(2):623–640CrossRefGoogle Scholar
  32. Johnson-Glenberg MC, Birchfield D, Megowan-Romanowicz C, Tolentino L, Martinez C (2009) Embodied games, next gen interfaces, and assessment of high school physics. Int J Learn Media 1(2)Google Scholar
  33. Kirschner PA, Sweller J, Clark RE (2006) Why minimal guidance during instruction does not work: an analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educ Psychol 41(2):75–86CrossRefGoogle Scholar
  34. Klahr D, Triona LM, Williams C (2007) Hands on what? The relative effectiveness of physical versus virtual materials in an engineering design project by middle school children. J Res Sci Teach 44(1):183–203CrossRefGoogle Scholar
  35. Latour B, Woolgar S (1979) Laboratory life: the social construction of scientific facts. Princeton University Press, PrincetonGoogle Scholar
  36. Lehrer R, Schauble L (2006) Cultivating model-based reasoning in science education. In: Sawyer K (ed) Cambridge handbook of the learning sciences. Cambridge, Cambridge University Press, pp 371–388Google Scholar
  37. Lesh R, Hoover M, Hole B, Kelly A, Post T (2000) Principles for developing thought-revealing activities for students and teachers. In: Kelly A, Lesh R (eds) Handbook of research design in mathematics and science education. Lawrence Erlbaum, Mahwah, pp 591–645Google Scholar
  38. Levy ST, Wilensky U (2008) Inventing a “mid-level” to make ends meet: reasoning through the levels of complexity. Cogn Instr 26(1):1–47CrossRefGoogle Scholar
  39. Liu X (2006) Effects of combined hands-on laboratory and computer modeling on student learning of gas laws: a quasi-experimental study. J Sci Educ Technol 15(1):89–100CrossRefGoogle Scholar
  40. Milgram P, Kishino F (1994) A taxonomy of mixed reality visual displays. IEICE Trans Inf Syst 77(12):1321–1329Google Scholar
  41. National Research Council (2012) A framework for k-12 science education: practices, crosscutting concepts, and core ideas. The National Academies Press, WashingtonGoogle Scholar
  42. Nersessian NJ (2005) Interpreting scientific and engineering practices: integrating the cognitive, social, and cultural dimensions. In: Gorman M, Tweney RD, Gooding D, Kincannon A (eds) Scientific and technological thinking. Lawrence Erlbaum, Hillsdale, pp 17–56Google Scholar
  43. NGSS Lead States (2013) Next generation science standards: for states, by states. The National Academies Press, WashingtonGoogle Scholar
  44. Norman D (1991) Cognitive artifacts. In: Carroll JM (ed) Designing interaction: psychology at the human-computer interface. Cambridge University Press, Cambridge, pp 17–38Google Scholar
  45. NRC (1996) National science education standards. National Academy Press, Washington (National Committee on Science Education Standards and Assessment)Google Scholar
  46. Olympiou G, Zacharia ZC (2012) Blending physical and virtual manipulatives: an effort to improve students’ conceptual understanding through science laboratory experimentation. Sci Educ 96(1):21–47CrossRefGoogle Scholar
  47. Papert S (1980) Mindstorms: children, computers, and powerful ideas. Basic Books, New YorkGoogle Scholar
  48. PASCO Scientific. From
  49. Perkins K, Adams W, Dubson M, Finkelstein N, Reid S, Wieman C, LeMaster R (2006) PhET: Interactive simulations for teaching and learning physics. Phys Teach 44:18CrossRefGoogle Scholar
  50. Radder H (2003) The philosophy of scientific experimentation. University of Pittsburgh Press, PittsburghGoogle Scholar
  51. Resnick M, Wilensky U (1998) Diving into complexity: developing probabilistic decentralized thinking through role-playing activities. J Learn Sci 7(2):153–171CrossRefGoogle Scholar
  52. Roth K, Garnier H (2006) What science teaching looks like: an international perspective. Educ Leadersh 64(4):16Google Scholar
  53. Rudolph JL (2005) Epistemology for the masses: the origins of “the scientific method” in American schools. Hist Educ Quart 45(3):341–376CrossRefGoogle Scholar
  54. Schwarz CV, White BY (2005) Metamodeling knowledge: developing students’ understanding of scientific modeling. Cogn Instr 23(2):165–205CrossRefGoogle Scholar
  55. Sipitakiat A, Blikstein P (2010) Robotics and environmental sensing for low-income populations: design principles, impact, technology, and results. In: Proceedings of the 9th International Conference of the Learning Sciences, Chicago, IL, pp 447–448Google Scholar
  56. Sipitakiat A, Blikstein P, Cavallo DP (2004) GoGo board: augmenting programmable bricks for economically challenged audiences. In: Proceedings of the 6th International Conference of the Learning Sciences, Santa Monica, CA, pp 481–488Google Scholar
  57. Smith GW, Puntambekar S (2010) Examining the combination of physical and virtual experiments in an inquiry science classroom. Paper presented at the Conference on Computer Based Learning in Science, Warsaw, PolandGoogle Scholar
  58. Stewart J, Cartier JL, Passmore CM (2005) Developing understanding through model-based inquiry. In: Donovan MS, Bransford JD (eds) How students learn: science in the classroom. National Academy Press, Washington, pp 515–565Google Scholar
  59. Thagard P (2007) Coherence, truth, and the development of scientific knowledge. Philos Sci 74(1):28–47CrossRefMathSciNetGoogle Scholar
  60. Tinker R (1991) History of probeware.
  61. Tinker R (ed) (1996) Microcomputer-based labs: educational research and standards. Springer, BerlinGoogle Scholar
  62. Treagust DF, Chittleborough GD, Mamiala TL (2002) Students’ understanding of the role of scientific models in learning science. Int J Sci Educ 24:357–368CrossRefGoogle Scholar
  63. Triona LM, Klahr D (2003) Point and click or grab and heft: comparing the influence of physical and virtual instructional materials on elementary school students’ ability to design experiments. Cogn Instr 21(2):149–173CrossRefGoogle Scholar
  64. van der Meij J, de Jong T (2006) Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment. Learn Instr 16(3):199–212CrossRefGoogle Scholar
  65. Vernier Software & Technology. From
  66. Wark T, Crossman C, Hu W, Guo Y, Valencia P, Sikka P, Corke P, Lee C, Henshall J, Prayaga K, O’Grady J, Reed M, Fisher A (2007) The design and evaluation of a mobile sensor/actuator network for autonomous animal control. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, Cambridge, MAGoogle Scholar
  67. Wilensky U (1999, updated 2006). NetLogo [Computer software]. Center for Connected Learning and Computer-Based Modeling, Evanston, IL. Retrieved from
  68. Wilensky U, Reisman K (2006) Thinking like a wolf, a sheep or a firefly: learning biology through constructing and testing computational theories. Cogn Instr 24(2):171–209CrossRefGoogle Scholar
  69. Windschitl M (2004) Folk theories of “inquiry:” How preservice teachers reproduce the discourse and practices of an atheoretical scientific method. J Res Sci Teach 41(5):481–512CrossRefGoogle Scholar
  70. Windschitl M, Thompson J, Braaten M (2008) Beyond the scientific method: model-based inquiry as a new paradigm of preference for school science investigations. Sci Educ 92:941–967. doi: 10.1002/sce.20259 CrossRefGoogle Scholar
  71. Zacharia ZC (2007) Comparing and combining real and virtual experimentation: an effort to enhance students’ conceptual understanding of electric circuits. J Comput Assist Learn 23(2):120–132CrossRefGoogle Scholar
  72. Zacharia ZC, Anderson OR (2003) The effects of an interactive computer-based simulation prior to performing a laboratory inquiry-based experiment on students’ conceptual understanding of physics. Am J Phys 71:618CrossRefGoogle Scholar
  73. Zacharia ZC, Olympiou G, Papaevripidou M (2008) Effects of experimenting with physical and virtual manipulatives on students’ conceptual understanding in heat and temperature. J Res Sci Teach 45(9):1021–1035CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Graduate School of EducationStanford UniversityStanfordUSA

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